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Gitelman, E.

Publications and source records attributed to Gitelman, E..

2 recordsLinked to original sources

CTCF-mediated cis-regulatory chromatin insulation enforces a central B-cell tolerance checkpoint

The generation of a diverse and self-tolerant B cell repertoire is essential for adaptive immunity and is achieved through V(D)J recombination. In mice, Ig{kappa} is the dominant light chain, whereas Ig{lambda} rearrangement typically occurs in response to nonproductive or autoreactive Ig{kappa} recombination, a process termed receptor editing. Recombination at the RS element deletes the Ig{kappa} constant exon, silencing the locus and enabling Ig{lambda} expression. However, the epigenetic regulatory framework that orchestrates and governs receptor editing remains poorly defined. Here, we identify a CTCF-binding insulator element (CBE) within the 3' Ig{kappa} super-enhancer (3'-SE{kappa}) that regulates receptor editing and directs the {kappa}-to-{lambda} switch required for Ig{lambda} B-cell development. Mechanistically, loss of this CBE activates an insulated enhancer within the 3'-SE{kappa}, causing aberrant V{kappa} rearrangements and altered chromatin interactions through disrupted loop extrusion dynamics. Notably, loss of this CBE in mice leads to increased autoantibody production by ten weeks of age, demonstrating that CBE-mediated chromatin architecture shapes B cell fate by constraining autoreactive potential. Collectively, our findings define a novel CTCF-dependent cis-regulatory insulation checkpoint that connects chromatin loop extrusion to antigen-driven receptor editing, thereby enforcing B-cell tolerance.

immunology↗

Computational analysis of morphological changes in Lactiplantibacillus plantarum under acidic stress

Cell shape and size often define characteristics of individual or communities of microorganisms in changing environments. Hence, characterizing cell morphology using computational image analysis can aid in the accurate identification of bacterial responses to these changes. Modifications in cell morphology of Lactiplantibacillus plantarum were determined in response to acidic stress, specifically during growth stage of the cells at pH 3.5 compared to pH 6.5. Consequently, we developed a computational method to sort, detect, analyze, and measure bacterial size in a single-species culture. We applied a deep learning methodology composed of object detection followed by image classification to measure the bacterial cell dimensions of the pre-identified cells. The results of our computational analysis show a significant change in cell morphology in response to alteration of environmental pH. Specifically, we found that the cell was dramatically elongated at low pH, while the width was not altered. Those changes could be attributed to modifications in membrane properties, for instance increased cell membrane fluidity in acidic pH. Integration of deep learning with microbial microscopic imaging is an advanced methodology for studying cellular structures. These trained models and scripts can be applied to other microbes and cells and are publicly available at: https://github.com/OraMoyal26/bacteria_dimensions/tree/main

systems biology↗